50+ days of Machine Learning

 Hello :) Today is Day 135! A quick summary of today: covered module 2 of the mlops-zoomcamp by DataTalks club about experiment tracking and model management cut 2 more videos for the Scottish dataset project Firstly, about using mlflow for MLOps Maybe this is because I am starting to learn about MLOps for the 1st time and I don't know other tools, but WOW mlflow is amazing. Below are my notes from the module 2 lectures. Full code on my github repo. First, some important concepts ML experiment: the process of building an ML model experiment run: each trial in an ML experiment run artifact: any file that is associated with an ML run experiment metadata What's experiment tracking? the process of keeping track of all the relevant info from an ML experiment (could include source code, environment, data, model, hyperparams, metrics, other - these can vary) Why is experiment tracking important? reproducability organization optimization Why can't we just use an excel spreadsheet...

 Hello :) Today is Day 134! A quick summary of today: covered the last 2 lectures of Stanford's CS109 processed 4 more videos for the Scottish accent dataset started MLOps zoomcamp by DataTalks club watched a nice video comparing the roles of data scientist vs AI engineer Firstly, on lecture 27 : advanced probability and lecture 28 : future of probability from Stanford's CS109 What a course! Sad it is over. Chris Piech - what an extraordinary professor!  Both lectures were more or less an overview of the course and professor Piech's hopes for the future of probability and his hopes for his students. Lecture 27 Around 2013, autograd was introduced for the first time and this allowed for backprop to be automated, instead of manual calculations. This was one of the reasons for deep learning to explode. The professor also talked a bit about diffusion models #1 remove the noise #2 predict where the noise is And a bit about language models (at the time of recording ChatGPT had ju...